Reinforcement learning is a promising approach to developing hard-to-engineer
adaptive solutions for complex and diverse robotic tasks. However, learning
with real-world robots is often unreliable and difficult, which resulted in
their low adoption in reinforcement learning research. This difficulty is
worsened by the lack of guidelines for setting up learning tasks with robots.
In this work, we develop a learning task with a UR5 robotic arm to bring to
light some key elements of a task setup and study their contributions to the
challenges with robots. We find that learning performance can be highly
sensitive to the setup, and thus oversights and omissions in setup details can
make effective learning, reproducibility, and fair comparison hard. Our study
suggests some mitigating steps to help future experimenters avoid difficulties
and pitfalls. We show that highly reliable and repeatable experiments can be
performed in our setup, indicating the possibility of reinforcement learning
research extensively based on real-world robots.

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